Business Development Manager ABB, NC, United States
Reliable and resilient power infrastructure equipment is essential to minimize disruptions and down-time of a semiconductor manufacturing facility: Power outages or fluctuations can disrupt the entire production line, leading to significant production losses.
In addition, cleanroom operations and wafers processing tools require significant power: Efficient power conversion and distribution systems can both save significant costs and help the industry re-duce its environmental footprint.
In that context, maintenance decisions of these electrical equipment are of the outmost importance,
ABB AI-enabled predictive maintenance solutions aim at reducing the amount of time required for the routine maintenance, increasing the time interval of maintenance and optimizing the maintenance activities. The total cost of ownership is therefore significantly reduced for the semiconductor facility manager.
In particular, ABB recently conducted market research to identify gaps perceived by maintenance man-agers and operators in the maintenance process of their electrification equipment. The research revealed that equipment owners typically have well-established processes for identifying general maintenance plans, dispatching technicians or service providers, and closing activities with reports. However, two common gaps were identified: While maintenance actions are often clearly defined, a prioritization process is frequently missing, leading to suboptimal distribution of the maintenance budget. Additionally, insights from maintenance activities included in reports are often lost in repositories and not utilized for further maintenance optimization.
The digital team at ABB Electrification Service took on the challenge of addressing these gaps using the latest digital technologies.
We propose a cloud-based system which consolidates data from inspection and maintenance activities as well as condition monitoring systems. Leveraging generative AI and machine learning algorithms, the system identifies risks of failures and gives recommendations how to mitigate them. Combined with the consequences of a potential failure, we provide clear priorities to maximize the effect of maintenance and modernization activities. The maintenance manager can easily schedule actions based on the prioritized recommendations.
We have selected a mobile application featuring an agent that would guide maintenance operators through step-by-step instructions and observation collection in a natural conversational style as an ideal match for acting upon the recommendations. Technologies like speech-to-text, text-to-speech, natural language understanding, and generative AI would find prominent application in this mobile app.
Capturing new observations during the operator’s activities, the circle closes: from observations to risks and recommendations and to actions which again lead to new observations.
